Ask HN: Why there is no Codecademy for ML or AI?

Yes, I did my research but there is no such interactive tutorial online like Treehouse or Codecademy. There are so many tutorials but none of it tells you the whole path.

Here are the resources I found useful:

========================================== Advices from Open AI, Facebook AI leaders

Courses You MUST Take: Machine Learning by Andrew Ng (https://www.coursera.org/learn/machine-learning) /// Class notes: (http://holehouse.org/mlclass/index.html)

Yaser Abu-Mostafa’s Machine Learning course which focuses much more on theory than the Coursera class but it is still relevant for beginners.(https://work.caltech.edu/telecourse.html)

Neural Networks and Deep Learning (Recommended by Google Brain Team) (http://neuralnetworksanddeeplearning.com/)

Probabilistic Graphical Models (https://www.coursera.org/learn/probabilistic-graphical-model...)

Computational Neuroscience (https://www.coursera.org/learn/computational-neuroscience)

Statistical Machine Learning (http://www.stat.cmu.edu/~larry/=sml/)

From Open AI CEO Greg Brockman on Quora

Deep Learning Book (http://www.deeplearningbook.org/) ( Also Recommended by Google Brain Team )

It contains essentially all the concepts and intuition needed for deep learning engineering (except reinforcement learning). by Greg

2. If you’d like to take courses: Linear Algebra — Stephen Boyd’s EE263 (Stanford) (http://ee263.stanford.edu/) or Linear Algebra (MIT)(http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebr...)

Neural Networks for Machine Learning — Geoff Hinton (Coursera) https://www.coursera.org/learn/neural-networks

Neural Nets — Andrej Karpathy’s CS231N (Stanford) http://cs231n.stanford.edu/

Advanced Robotics (the MDP / optimal control lectures) — Pieter Abbeel’s CS287 (Berkeley) https://people.eecs.berkeley.edu/~pabbeel/cs287-fa11/

Deep RL — John Schulman’s CS294–112 (Berkeley) http://rll.berkeley.edu/deeprlcourse/

From Director of AI Research at Facebook and Professor at NYU Yann LeCun on Quora

In any case, take Calc I, Calc II, Calc III, Linear Algebra, Probability and Statistics, and as many physics courses as you can. But make sure you learn to program.

Codecademy's model tends to be very poor for topics that are sufficienty theoretically complex, as the turnaround time between exercise beginning and conclusion appears to be about 5 minutes for non-project work.

Repetition/drilling for understanding and examination of knowledge by self-testing are wholly absent.

Projects like "free code camp" can dodge this problem because the staff is working for free as an open source project, but it would take a lot more 5 minute segments to teach someone linear algebra, the theory behind any type of model, and the background information that is necessary to generate provably compelling insight than companies like Codeacademy seem interested in tackling.

It's called an M.Sc.

What does physics have to do with ML/AI?